Hello. I was wondering if in the event that you are performing an EFA and receive a warning that there is a Heywood case, is computing the EFA in a CFA framework the only way you can deal with this? Thank you.

As a follow-up question I also wanted to know if in the event that Mplus tells you which specific variable is causing a Heywood case, then should you delete that particular variable? If this is incorrect, what are one's other options?

Computing an EFA in a CFA framework or using our new EFA feature described in the Version 5.1 Examples Addemdum would still result in a negative residual variance. If it is small and non-signficant, you could fix it to zero in this setting. Removing the variable will probably not help. You should reduce the number of factors or increase your sample size.

I have conducted an EFA with count indicators. I do not appear to have a Heywood case, but I have a near Heywood case in that all of the residual error variances are vanishingly small and insignificant. The count indicators themselves have very small means (ranging from 1/5 to 1/20), but the sample size is large (5570). The same situation occurs for one, two and three factors, and when I conduct a CFA the residual variances are not included in the output. Otherwise, the estimates seem reasonable.

Are these results to be trusted? Is there any way to improve the estimates?